Made byAziz Zena
Naina Grewal
Amey Sonawane
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Abstract
Introduction
Motivation
Problem Definition
Methodologies
Conclusion
Future Work
References
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With the high speed of development in national
economy and quickening of the urbanization process
follow big increase in urban traffic which create
imbalance of supply and demand of transportation
system of traffic
Number of private car was increase which cause
high traffic volume , increase number of accident
and traffic jam.
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Traffic jam is one of the many hazards, people face
everyday.
High traffic volume, construction, accidents,
unexpected emergencies, events and visual
obstructions are some main causes of traffic
congestion
A lot of methods are present to solve this problem .
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Traffic congestion is one of the problems which
everyone faces in his/her daily routine and people
get stuck in Traffic almost everyday and waste their
valuable time. Sometimes there is a case of
emergency and you are stuck in Traffic.
When you are stuck in a Traffic Jam you will be
inhaling all the gases coming out of the vehicles
stuck there with engines running.
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After the big development in economy field number
of private car was increase, traffic jams get more
severe, traffic accidents become more frequently
and traffic environment worsens. Which bring a
huge pressure to urban traffic .
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Investigate a novel road pricing model to prevent
and reduce the traffic congestion in urban areas
The road prices are changed dynamically according
to both the traffic densities and popularities of the
roads
road density, road capacity, the average speed of
vehicle in the road, the destination parameter,
vehicle type these parameters indicate the road
selection criterions for a driver .
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So the road with high density turn to have high toll
fee.
Driver at a junction tends to follow a road having a
low price, the traffic congestion at the roads with
high traffic density is prevented and reduced
because of the higher price of these roads.
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It will need a dynamic road pricing model because it
is impossible to pricing the entirely road on the
route preference of vehicle users
The pricing is instantaneous two vehicles that
consecutively enter a road can be charged
differently
The users have the pricing information of only the
next alternative roads when they arrive at a junction.
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It addressed the problem of finding the shortest path
under traffic jams in road network
Define two key concepts
-Road network
-Speed pattern
Present improved algorithm by storage discard
routes and classify them during the query process to
get more spare routes.
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The test results show that the incremental algorithm
is reliable and highly effective for Optimum path
planning
The performance of algorithm was improved .
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They propose a new method through each vehicle
independently performs the following actions.
Each vehicle periodically broadcasts a request message
through a vehicle network (VANET) to obtain information of
vehicles in the limited area around the vehicle
Each receiving side vehicle replies a response which includes
information about the vehicle (vehicle ID, velocity of the
vehicle, roadway segment ID in which the vehicle exists.
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The sending side vehicle receives response from the
receiving side vehicles and evaluates each roadway segments
based on the responses.
The sending side vehicle calculates a route for a destination
of the sending side vehicle
Function to calculate the congestion degree where degree’s
value becomes zero when congestion levels of all areas are
equal and degree’s value becomes high when congestion
levels of neighboring areas are different. At first the whole
driving field is divided into some areas. The traffic
congestion degree is assigned to each area at each moment.
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Each vehicle will need a wireless function in order to form a
network
Each vehicle need GPS or function showing ID of road way
segment plus each vehicle need a map road information in
order to show a receiving side vehicle position to the road
map
If each vehicle obtains traffic information of region which is
small, many vehicles would fail to find non-congested area.
So existing congested traffic flows are not solved efficiently.
If each vehicle obtains traffic information of region which is
too wide, many vehicles would head to a non-congested area.
So new congested traffic flows would happen at the noncongested area.
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GIS is Geographical Information Systems. computer system
designed to capture, store, manipulate, analyze, manage, and
present all types of geographical data. The acronym GIS is
sometimes used for geographical information
science or geospatial information studies to refer to the
academic discipline or career of working with
geographic information systems and is a large domain within
the broader academic discipline of Geoinformatics.
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Analyzing large quantities of data, such as statistics for
individuals or buildings, across a geographic area.
Analyzing several different kinds of data across an area
and understanding how they relate to one another e.g.
property types, employment patterns and property
values for a given neighbourhood .
Analyzing changes to data over time and visualising the
results to allow their ready comparison; projections of
future scenarios can be incorporated in the same way,
although using any model to predict the future is highrisk.
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Visualizing the results of analysis, to allow even non-expert
users to understand them easily
Making it easier to spot errors and anomalies, smoothing out
the effects of micro-scale phenomena and creating the most
accurate possible picture of what’s at work.
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Its technical nature can make results appear more reliable
than they are; poor operators can hide assumptions and errors
in a composite results, while users can be ‘blinded with
science’ and not apply their usual standards of questioning to
what they are being told.
The results of a GI analysis can only ever be as accurate as
the data which underlies them, and should only ever be
reported at the finest spatial scale of any dataset used.
The availability of data at the required scale at a reasonable
cost is a universal issue.
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Real Time GIS with MapInfo and SCOOT.
Traffic Congestion Information Promulgating System.
GIS based Intelligent Traffic System.
GIS based Transport Decision Support System.
Traffic Map Editor Using GPS with Shortest Path Algorithm.
Traffic Incident Information Management System.
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This system facilitates the use of geographical data in the
context of time-varying information and integrates traffic
data as a new component of GIS.
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The GIS database integrates historical and current traffic
states within appropriate network components.
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Traffic data are overlaid on urban maps or geographical
reference. The application takes advantages of existing
software such as the GIS MapInfo and traffic management
system SCOOT.
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This system has been develop by using VB6.0 and MapX5.0.
This system can intuitively and visualization provides realtime traffic congestion information for traffic managers and
users, e.g. the queue lengths at intersections.
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This system can realize the functions of dynamic
management and analyze of the traffic congestion
information, which can display the spatial places on the map
vividly.
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This is a software that integrates control, management and
decision-making.
It is designed and developed for the modernized traffic
command center.
It works by utilizing the advanced information process
technology, navigation technology, wireless communication
technology, automatic control technology, image analysis
technology and computer network technology.
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This system as the functions of designing traffic networks on
digital maps and doing traffic equilibrium analysis as well as
a novel function to integrate local detailed structures of
intersections into global networks. The latter is particularly
useful for the analysis of large traffic networks.
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To minimize the time of map showing o editing, this system
is using bucket-based method, which separates the data as
rectangular unit(bucket) to index and describes by layer.
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There are two different applications in this system which we
are using to solve the problem.
One to take data from the real traffic and the other one to
find the optimum route or a daily driver.
All this data are also taken as input for the central distributed
applications that manages to analyze them and to offer a
starting point in predicting traffic congestions and solve them
by using the already existing resources in the infrastructures.
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GIS-T and traffic information platform is the base of TIIMS.
It also provides traffic incident information and geographical
information.
This system platform is MapInfo as MapInfo has many
functions such as strong graph handling, statistics analysis,
query and option, and can visit various databases such as
SQL, Oracle, Sybase and Informix.
Thus TIIMS can provide multiple input types, and save,
query, analyze and show incident information.
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A route planning system subsystem is responsible for determining
optimum route between user specified origin and destination.
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Much of the capability and functionality of route planning derived
from database which has specially designed to facilitate path planning
problem.
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Provides In-vehicle route guidance.
Criteria for optimum route planning
-Minimum time.
-Minimum distance.
-Minimum Turns(i.e. Maneuvers or instructions).
-Avoiding or encouraging freeways.
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For In-vehicle guidance, two scenarios considered
“Strategic” route planning and “Tactical” route
planning.
 Three major components of route planning subsystem to
solve problem.
-A database with sufficient breadth of information.
-A modified A* graph search procedure for searching road
network to determine optimum route.
-A database structure and interface which enables A*
algorithm to efficiently access all necessary data.
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A* graph technique introduced by Nisson which guarantee to
find optimum route through graph, performs directed breadth
first search from source to destination.
 Terms used in this algorithm
-Node
-Segment
-Path
-Every node in search path has a cost ‘f’ and defined as
f(n) = g(n) + h(n)
The term g(n) is ‘known’ cost get from source to node n and
h(n) is heuristic estimate(intelligent guess) of the cost of path
from node n to the destination.
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Optimization criteria:-The ‘g’ cost is know cost from source to the current node.
-The ‘g’ cost includes weighted combination of following
-Segment Speed limit, Segment distance, Traffic signals and
stop time delays, Type of segment( freeway, artery, ramp,
street etc), turning delays at an intersection.
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A* algorithm considered to be good for finding optimum
path when In-vehicle guidance, so In future it can be used for
to find optimum path when traffic jam occurs.
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Ant colony optimization is meta heuristic based on colony of
artificial ants which work co-operatively, building solutions
by moving on the problem graph and by communicating
through artificial pheromone trails mimicking real ants.
It is multi agent multi heuristic technique where artificial
ants built better solution by communication through artificial
pheromone imitating real ants.
ACO algorithms are construction algorithm where every ant
constructs a solution to the problem by travelling on a
construction graph in each iteration.
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Which aims at choosing an alternative optimum path to avoid
traffic jams and then resuming that same path again when the
traffic is regulated.
Traffic jam is detected through pheromone values on edges
which are updated according to goodness of solution on the
optimal tours only.
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Approach :
A weighted connected graph is taken as input.
Nodes represents different places and weighted edges
represents distance between places.
Edge has two types of information one is ‘physical distance’
and other ‘artificial pheromone trail information’, both the
information are combined to select next node travel by ant.
Goal is to travel optimal path.
Physical distance:-actual distance between two nodes
 Node Selection:-To ensure exploration of maximum, paths a random function is
used in addition to probability function.
-Probability function of ant ‘k’ at node ‘i’ need to travel to node
‘j’ at time ‘t’ is given by,
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Where ‘τij(t)’ is intensity of pheromone trail on edge (i,j) at time ‘t’.
N is number of nodes
tabu(k) is dynamically growing vector containing the nodes
already visited by ant(k)
allowed(k)={ N- tabu(k)},
ἠij(visibility factor)= 1/dij.( dij distance between nodes I and j)
α,β are the parameters that control the relative importance of
pheromone trail vs visibility.
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Experiments shows that distance increases when there is
traffic jams as compare to situation when there is no traffic
jams or traffic is normalized.
This approach successively computes alternative optimum
path to avoid traffic jams.
MMAS-MDS algorithm( Max/ Min Ant system
extended by multidimensional scaling).
- This algorithm for solving travelling salesman problem
more effectively in congested traffic network.
 MMAS has several characteristic which make it an
excellent ACO method.
1)The range of pheromone trail on each edge is limited to
an interval [τmin,τmax]
2)It exploits the best solution by only allowing the
iteration-best ant or best-so-far ant to add pheromone.
3) Re- initialization of the pheromone trail occurs
occasionally to provide higher exploration of solution.
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MDS is a methodology that takes the proximity
measurements of object pairs as inputs and represent
configuration of points as distance in m-dimensional space.
Though MMAS perform well to solve TSP in Euclidean
distance but it will not provide optimal solution.
The main reason is that heuristic information in the ant tour
construction only concerns the distance in the neighborhood
of city i regardless of global guides.
So time-space configuration constructed by MDS provides
good and effective global guide.
If there are n cities, then all the time relationships of n(n-1)/2
pairs of cities are involved in MDS computation.
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MMAS-MDS has advantage that not only considers the
promising paths with local heuristic information but also
considers global promising routes.
Experiments shows that optimum path found by MMASMDS algorithm is much better than MMAS algorithm in
congested transportation system.
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Several methods have been implemented in order to avoid
traffic congestion but it can’t be completely avoided as the
number of vehicles and population in every metropolitan
cities is increasing multiple times.
A powerful platform –Geographic Information System has
been introduced that take full account of spatial
characteristics of traffic information.
The system can promulgate and forecast the real time
operating status of urban traffic and analyze the trend of
regional traffic safety.
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It improves the efficiency of transportation sector so that
traffic resources are fully utilized.
Various congestion activities are also used such as
redesigning traffic signal timing to improve progressive
traffic movements along road ways, road widening projects
to be implemented to improve the service.
Constructing turning lanes at critical intersections to separate
turning and through traffic.
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Road pricing is efficient and environmentally beneficial
available tool for congested cities.
However, road pricing cannot by itself deal with the transport
problems. It must be seen as a part of comprehensive policy
package, which includes substantial improvements to public
transport and other alternative modes, environmental
enhancements, and in the long term, new approaches to land
use planning and more usages of intelligent transport
systems.
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The simulation results of the road pricing method shows that
the pricing based traffic congestion algorithm proposed in
this paper homogenizes the traffic densities over the entire
traffic network and traffic congestion can be prevented.
Lately VANET method has also been used for alleviating
traffic congestion in urban transportations. Simulation results
showed that this method is effective in terms of velocity and
trip time of vehicles in environment that traffic varies
temporally and spatially .
The method should be improved by controlling how much
each vehicle collects information on traffic congestion.
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C.Claramunt,E.Peytchev and A.Bargiela “A real time GIS for
the analysis of a traffic System”.
Jingang Bao,Jiangqian Ying. “Development of Traffic
Analysis System using GIS”.
Shao Fei, Deng Wei ,Zhang Bing. “Traffic information
Mnaggemnt and promulgationg system based on GIS”.
Guiyan Jiang,Mingchen Gu, Guohua Han and Xianping
Yang “Traffic Incident Information Mangement Systems
based on GIS-T”.
Bing Zhang,Wei Deng and Ling Mao.”Traffic congestion
Information Promulgating System based on GIS-T”.
Fahri Soylemezgiller,Murat Kuscu and Deniz Kilinc.”A
traffic Congestion avoidance algorithm with dynamic road
pricing for smart cities “.
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Emilian Necula,Raluca Necula and Adrian Iftene.”A gis
integrated solution for traffic management”.
Zhiheng Li,Kezhu SONG,and Zhiganag QIU”Integrated
Traffic Management Platform Design Based on GIS-T”.
Xiao Juan,Ye Fing .“A study on the Framework of GIS-based
Basic Traffic Management System”.
Tu Shengwu.“Design of road traffic safety evaluation system
based on GIS-T.”
Xiayu Song ,Lanyang Yu,Huanliang Sun.“An incremental
query algorithm for optimal path queries under traffic jams”.
Mitsuhisa Kimura, Yousuke Taoda,Yoshiaki Kakuda and
Tadishi Dohi.“A novel based method on VANET for
alleviating Traffic Congestion in urban transportations”